An Ensemble Approach Integrating Retrieval-Augmented Large Language Models and Boosting Algorithms for Enhanced Catatonia Phenotyping Article Swipe
Yubo Feng
,
Ruiyan Ma
,
Xinmeng Zhang
,
You Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3233/shti250920
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3233/shti250920
A critical first step in using large-scale data to study catatonia is the development of precise phenotyping algorithms that can identify instances of the condition. In this work, we present an ensemble approach that combines retrieval-augmented generation (RAG) large language models (LLMs) with boosting algorithms to phenotype catatonia from the electronic health records (EHRs) of 3.5 million individuals seen at a large academic medical center from 2006 to 2017. Although the ensemble model achieved an AUROC of 0.709, slightly lower than the boosting algorithm alone (AUROC = 0.713), the inclusion of the RAG-LLM component provides enhanced interpretability. In particular, the RAG-LLM can identify contextually complex clinical features, such as those described by the Bush–Francis Catatonia Rating Scale, directly from clinical notes. These results highlight the potential of RAG-LLMs to capture nuanced contextual cues and fulfill complex catatonia phenotype definitions, even when overall classification performance is comparable to more traditional machine learning methods.
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Metadata
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- OA Status
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4413039982Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3233/shti250920Digital Object Identifier
- Title
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An Ensemble Approach Integrating Retrieval-Augmented Large Language Models and Boosting Algorithms for Enhanced Catatonia PhenotypingWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-08-07Full publication date if available
- Authors
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Yubo Feng, Ruiyan Ma, Xinmeng Zhang, You ChenList of authors in order
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https://doi.org/10.3233/shti250920Publisher landing page
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.3233/shti250920Direct OA link when available
- Concepts
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Boosting (machine learning), Computer science, Catatonia, Artificial intelligence, Machine learning, Algorithm, Schizophrenia (object-oriented programming), Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.machine | 149 |
| abstract_inverted_index.medical | 63 |
| abstract_inverted_index.million | 56 |
| abstract_inverted_index.nuanced | 130 |
| abstract_inverted_index.overall | 141 |
| abstract_inverted_index.precise | 15 |
| abstract_inverted_index.present | 29 |
| abstract_inverted_index.records | 52 |
| abstract_inverted_index.results | 122 |
| abstract_inverted_index.Although | 69 |
| abstract_inverted_index.RAG-LLMs | 127 |
| abstract_inverted_index.academic | 62 |
| abstract_inverted_index.achieved | 73 |
| abstract_inverted_index.approach | 32 |
| abstract_inverted_index.boosting | 43, 82 |
| abstract_inverted_index.clinical | 105, 119 |
| abstract_inverted_index.combines | 34 |
| abstract_inverted_index.critical | 1 |
| abstract_inverted_index.directly | 117 |
| abstract_inverted_index.enhanced | 95 |
| abstract_inverted_index.ensemble | 31, 71 |
| abstract_inverted_index.identify | 20, 102 |
| abstract_inverted_index.language | 39 |
| abstract_inverted_index.learning | 150 |
| abstract_inverted_index.methods. | 151 |
| abstract_inverted_index.provides | 94 |
| abstract_inverted_index.slightly | 78 |
| abstract_inverted_index.Catatonia | 114 |
| abstract_inverted_index.algorithm | 83 |
| abstract_inverted_index.catatonia | 10, 47, 136 |
| abstract_inverted_index.component | 93 |
| abstract_inverted_index.described | 110 |
| abstract_inverted_index.features, | 106 |
| abstract_inverted_index.highlight | 123 |
| abstract_inverted_index.inclusion | 89 |
| abstract_inverted_index.instances | 21 |
| abstract_inverted_index.phenotype | 46, 137 |
| abstract_inverted_index.potential | 125 |
| abstract_inverted_index.algorithms | 17, 44 |
| abstract_inverted_index.comparable | 145 |
| abstract_inverted_index.condition. | 24 |
| abstract_inverted_index.contextual | 131 |
| abstract_inverted_index.electronic | 50 |
| abstract_inverted_index.generation | 36 |
| abstract_inverted_index.development | 13 |
| abstract_inverted_index.individuals | 57 |
| abstract_inverted_index.large-scale | 6 |
| abstract_inverted_index.particular, | 98 |
| abstract_inverted_index.performance | 143 |
| abstract_inverted_index.phenotyping | 16 |
| abstract_inverted_index.traditional | 148 |
| abstract_inverted_index.contextually | 103 |
| abstract_inverted_index.definitions, | 138 |
| abstract_inverted_index.Bush–Francis | 113 |
| abstract_inverted_index.classification | 142 |
| abstract_inverted_index.interpretability. | 96 |
| abstract_inverted_index.retrieval-augmented | 35 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.59938734 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |